scholarly journals Evolution of Habitat Quality and Its Topographic Gradient Effect in Northwest Hubei Province from 2000 to 2020 Based on the InVEST Model

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 857
Author(s):  
Mengyao Li ◽  
Yong Zhou ◽  
Pengnan Xiao ◽  
Yang Tian ◽  
He Huang ◽  
...  
Keyword(s):  
Land Use ◽  
Habitat Quality ◽  
Cultivated Land ◽  
Topographic Position ◽  
Invest Model ◽  
Topographic Position Index ◽  
Gradient Effect ◽  
Topographic Gradient ◽  
Position Index

Regional land use change and ecological security are important fields and have been popular issues in global change research in recent years. Regional habitat quality is also an important embodiment of the service function and health of ecosystems. Taking Shiyan City of Hubei Province as an example, the spatiotemporal differences in habitat quality in Shiyan City were evaluated using the habitat quality module of the InVEST model and GIS spatial analysis method based on DEM and land use data from 2000, 2005, 2010, 2015, and 2020. According to the habitat quality index values, the habitats were divided into four levels indicating habitat quality: I (very bad), II (bad), III (good), and IV (excellent), and the topographic gradient effect of habitat quality was studied using the topographic position index. The results show the following. (1) The habitat quality of Shiyan City showed relatively high and obvious spatial heterogeneity overall and, more specifically, was high in the northwest and southwest, moderate in the center, and low in the northeast. The higher quality habitats (levels III, IV) were mainly distributed in mountain and hill areas and water areas, while those with lower quality habitats (levels I, II) were mainly distributed in agricultural urban areas. (2) From 2000 to 2020, the overall average habitat quality of Shiyan City first increased, then decreased, and then increased again. Additionally, the habitat area increased with an improvement in the level. There was a trend in habitat transformation moving from low to high quality level, showing a spatial pattern of “rising in the southwest and falling in the northeast”. (3) The habitat quality in the water area and woodland area was the highest, followed by grassland, and that of cultivated land was the lowest. From 2000 to 2020, the habitat quality of cultivated land, woodland, and grassland decreased slightly, while the habitat quality of water increased significantly. (4) The higher the level of the topographic position index, the smaller the change range of land use types with time. The terrain gradient effect of habitat quality was significant. With the increase in terrain level, the average habitat quality correspondingly improved, but the increasing range became smaller and smaller. These results are helpful in revealing the spatiotemporal evolution of habitat quality caused by land use changes in Shiyan City and can provide a scientific basis for the optimization of regional ecosystem patterns and land use planning and management, and they are of great significance for planning the rational and sustainable use of land resources and the construction of an ecological civilization.

scholarly journals Multi-Scenario Analysis of Habitat Quality in the Yellow River Delta by Coupling FLUS with InVEST Model

2021 ◽  
Vol 18 (5) ◽  
pp. 2389
Author(s):  
Qinglong Ding ◽  
Yang Chen ◽  
Lingtong Bu ◽  
Yanmei Ye
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Habitat Quality ◽  
Scenario Analysis ◽  
Yellow River ◽  
The Yellow River ◽  
Cultivated Land ◽  
Invest Model ◽  
Unused Land ◽  
Dongying City

The past decades were witnessing unprecedented habitat degradation across the globe. It thus is of great significance to investigate the impacts of land use change on habitat quality in the context of rapid urbanization, particularly in developing countries. However, rare studies were conducted to predict the spatiotemporal distribution of habitat quality under multiple future land use scenarios. In this paper, we established a framework by coupling the future land use simulation (FLUS) model with the Intergrated Valuation of Environmental Services and Tradeoffs (InVEST) model. We then analyzed the habitat quality change in Dongying City in 2030 under four scenarios: business as usual (BAU), fast cultivated land expansion scenario (FCLE), ecological security scenario (ES) and sustainable development scenario (SD). We found that the land use change in Dongying City, driven by urbanization and agricultural reclamation, was mainly characterized by the transfer of cultivated land, construction land and unused land; the area of unused land was significantly reduced. While the habitat quality in Dongying City showed a degradative trend from 2009 to 2017, it will be improved from 2017 to 2030 under four scenarios. The high-quality habitat will be mainly distributed in the Yellow River Estuary and coastal areas, and the areas with low-quality habitat will be concentrated in the central and southern regions. Multi-scenario analysis shows that the SD will have the highest habitat quality, while the BAU scenario will have the lowest. It is interesting that the ES scenario fails to have the highest capacity to protect habitat quality, which may be related to the excessive saline alkali land. Appropriate reclamation of the unused land is conducive to cultivated land protection and food security, but also improving the habitat quality and giving play to the versatility and multidimensional value of the agricultural landscape. This shows that the SD of comprehensive coordination of urban development, agricultural development and ecological protection is an effective way to maintain the habitat quality and biodiversity.

scholarly journals Study On Spatio-Temporal Evolution of Habitat Quality Based On Land-Use Change In Chongming Dongtan, China

2021 ◽  
Author(s):  
Sheng Li ◽  
Bin Dong ◽  
Xiang Gao ◽  
Haifeng Xu ◽  
Chunqiu Ren ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Habitat Quality ◽  
Migratory Birds ◽  
Habitat Degradation ◽  
Water Area ◽  
Ecological Environment ◽  
Cultivated Land ◽  
Construction Land ◽  
Invest Model

Abstract Chongming Dongtan is an important habitat for international migratory birds. It is of great significance to study its land use and habitat quality change for rare waterfowl protection and ecological environment restoration. Based on the land use data in 2002, 2012 and 2020, this paper analyzes the relationship between land use change and habitat quality evolution in Chongming Dongtan in recent 18 years by using InVEST model, land use dynamic degree and land use degree index. The results show that the main types of land use in the study area are water area and cultivated land, followed by woodland, reed beach, grass beach, bare beach and construction land. Among them, bare beach and construction land have changed dramatically, the former continuing to decrease while the latter continuing to increase. And the increasing speed began to slow down after 2012, and the increasing part was mainly from the surrounding cultivated land. Secondly, in terms of time change, the degradation of habitat quality in Chongming Dongtan has been gradually improved from aggravating trend in the past 18 years. In terms of spatial distribution, the habitat degradation degree of Chongming Dongtan is higher in the east and lower in the west, spreading from the center to the surrounding. Based on this, the change of land use and the interference of human activities are the important reasons for the change of ecological environment quality.

Application of the topographic position index to heterogeneous landscapes

Geomorphology ◽  
2013 ◽  
Vol 186 ◽  
pp. 39-49 ◽  
Author(s):  
Jeroen De Reu ◽  
Jean Bourgeois ◽  
Machteld Bats ◽  
Ann Zwertvaegher ◽  
Vanessa Gelorini ◽  
...  
Keyword(s):  
Topographic Position ◽  
Topographic Position Index ◽  
Heterogeneous Landscapes ◽  
Position Index

scholarly journals Estimating Changes in Habitat Quality through Land-Use Predictions: Case Study of Roe Deer (Capreolus pygargus tianschanicus) in Jeju Island

2020 ◽  
Vol 12 (23) ◽  
pp. 10123
Author(s):  
Dong-jin Lee ◽  
Seong Woo Jeon
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Protected Areas ◽  
Habitat Quality ◽  
Green Infrastructure ◽  
Roe Deer ◽  
Land Use Changes ◽  
Jeju Island ◽  
Invest Model ◽  
Integrated Valuation

This study predicts future land-use changes and the resulting changes in habitat quality, suggesting a method for establishing land-use management to ensure sustainable wildlife habitats. The conservation effects were verified in terms of wild animal habitat quality according to the designation of protected areas. Land-use change until 2050 was predicted using the Dyna-Conversion of Land Use Change and its effects (Dyna-CLUE) model for Jeju Island, Korea, and the change in the quality of roe deer habitats was predicted using the Integrated Valuation and Environmental Services and Tradeoffs (InVEST) model. Results indicate that, compared to 2030, urbanized area increased by 42.55 km2, farmland decreased by 81.36 km2, and natural area increased by 38.82 km2 by 2050. The average habitat quality on Jeju Island was predicted to decrease from 0.306 in 2030 to 0.303 in 2050. The average habitat quality ranged from 0.477 in 2030 to 0.476 in 2050 in protected areas and 0.281 in 2030 to 0.278 in 2050 outside protected areas. Habitat quality in protected areas was relatively high, and its reduction was limited. Areas with lower habitat quality need approaches such as expanding greenery and improving its quality. By establishing appropriate land-use plans by predicting habitat quality, wildlife habitats can be better maintained and protected, which is a primary goal of green infrastructure.

scholarly journals A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

2021 ◽  
Vol 13 (18) ◽  
pp. 3557
Author(s):  
Marc Wehrhan ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Errors ◽  
Field Scale ◽  
Local Approach ◽  
Topographic Position ◽  
Topographic Position Index ◽  
Soil Landscape ◽  
Position Index

Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

Mapping and assessment of future land use change impacts on habitat quality in Lithuania

2021 ◽  
Author(s):  
Paulo Pereira ◽  
Eduardo Gomes ◽  
Miguel Inacio ◽  
Katarzyna Bogdzevič ◽  
Donalda Karnauskaite ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Habitat Quality ◽  
Agricultural Intensification ◽  
Land Use Changes ◽  
Land Abandonment ◽  
Comparison Function ◽  
Invest Model ◽  
Landscape Degradation ◽  
Social Fund

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Human activity is directly responsible for land use and land cover changes, affecting different ecosystem services. Thus, from the perspective of land use management is critical to project potential future land-use changes. This study aimed: (i) to detect possible changes in land-use structure in response to different four scenarios, namely: business as usual, urbanization, afforestation and land abandonment, and agricultural intensification scenario; and (ii) to measure the landscape habitat quality (an ecosystem services proxy) according to those projected futures. We selected as case study Lithuania due to the potential future increased human pressures on the landscape, and due to the high landscape value of this territory. The projected year was 2050, and we used the Cellular Automata method (applying the Dinamica EGO software) to project future land-use changes, and the InVEST model to assess the habitat quality. The land-use scenarios outcomes were validated using a fuzzy comparison function, and 80% of accuracy was achieved (comparing a simulated land use map of 2018, and the observed map for the same year). The results showed that the agricultural intensification scenario represents the greatest predicted landscape deterioration (from 0.71 in 2018 to 0.64). In the urbanization scenario, the highest landscape degradation prediction is identified around the most important cities (Vilnius, Kaunas, and Klaip&amp;#279;da). In the opposite direction, the afforestation and land abandonment scenario show the highest improvement on the habitat quality, from 0.71 in 2018 to 0.74.&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#8220;Lithuanian National Ecosystem Services Assessment and Mapping (LINESAM)&amp;#8221; No. 09.3.3-LMT-K-712-01-0104 is funded by the European Social Fund according to the activity &amp;#8220;Improvement of researchers&amp;#8217; qualification by implementing world-class R&amp;D projects&amp;#8221; of Measure No. 09.3.3-LMT-K-712.&lt;/p&gt;

Identifying Small Depressional Wetlands and Using a Topographic Position Index to Infer Hydroperiod Regimes for Pond-Breeding Amphibians

Wetlands ◽  
2017 ◽  
Vol 37 (2) ◽  
pp. 325-338 ◽  
Author(s):  
Jeffrey W. Riley ◽  
Daniel L. Calhoun ◽  
William J. Barichivich ◽  
Susan C. Walls
Keyword(s):  
Depressional Wetlands ◽  
Topographic Position ◽  
Topographic Position Index ◽  
Position Index
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